Teaching

Linköping University

• TDDE78 - Reinforcement Learning (Lab Responsible), 2024
  Responsible for laboratory sessions in this 6-credit advanced course on reinforcement learning. I design and supervise hands-on laboratory assignments based on scientific literature in reinforcement learning, covering fundamental concepts and state-of-the-art algorithms. Students work on practical implementations of RL algorithms, gaining experience with both theoretical understanding and practical skills. The labs focus on research-oriented projects that help students develop capabilities in planning, conducting, and evaluating research and development projects in the field of reinforcement learning.
• TDDE13 - Multiagent Systems (Teaching Assistant), 2024
  Responsible for laboratory sessions and seminars in this advanced course on multi-agent systems. The course covers agentic AI with hands-on labs on building LLM agents using local open-source models (Ollama with llama3.2) and multi-agent communication and coordination. Additionally, I supervise multi-agent reinforcement learning laboratory sessions where students implement and experiment with MARL algorithms. I also facilitate seminars on agents and game theory, mechanism design, auctions, and social choice, guiding students through exercise sets and providing feedback on their individual research reports. The course emphasizes both theoretical foundations of multi-agent systems and practical implementation skills, preparing students for research and industry applications in collaborative AI systems.

Dakar Institute of Technology (DIT)

• Reinforcement Learning course (Master2 class), 2023-2024
  Taught a comprehensive Master’s level course covering fundamental concepts and advanced techniques in reinforcement learning. The course included theoretical foundations of Markov Decision Processes (MDPs), value-based methods (Q-learning, DQN), policy-based methods (REINFORCE, Actor-Critic), and deep reinforcement learning algorithms. Students gained hands-on experience implementing RL algorithms using TensorFlow and PyTorch, working on classic environments like CartPole and MountainCar, as well as more complex tasks. The course emphasized both theoretical understanding and practical implementation skills, preparing students for research and industry applications in autonomous systems, robotics, and intelligent decision-making.
• Computer Vision course (Master1 class), 2023
  Designed and delivered a Master’s level course on computer vision covering fundamental image processing techniques and state-of-the-art deep learning methods. The curriculum included image filtering and edge detection, introduction to Convolutional Neural Networks (CNNs), PyTorch tutorials, image classification, object detection, image segmentation (semantic and instance segmentation), optical flow, and action recognition. Students learned to implement and train deep learning models for various computer vision tasks, gaining practical experience with modern frameworks and datasets. The course combined theoretical foundations with hands-on projects, enabling students to develop real-world computer vision applications.